R Tutorial
An introduction to R
Introduction
This tutorial is will introduce the reader to
,
a free, open-source statistical computing environment often used with
RStudio, a integrated development environment for
.
R Project Logo
Download
Download at https://www.r-project.org/
Download RStudio at https://rstudio.com/products/rstudio/download/
Calculator
can be used as a super awesome calculator
# 5 + 3 = 8
5 + 3 ## [1] 8
# 24 / (1 + 2) = 8
24 / (1 + 2) ## [1] 8
# 2 * 2 * 2 = 8
2^3 ## [1] 8
# 8 * 8 = 64
sqrt(64) ## [1] 8
# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000) ## [1] 8
Functions
has many useful built in functions
1:10## [1] 1 2 3 4 5 6 7 8 9 10
as.character(1:10)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
rep(1:2, times = 5)## [1] 1 2 1 2 1 2 1 2 1 2
rep(1:5, times = 2)## [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2)## [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, length.out = 7)## [1] 1 2 3 4 5 1 2
seq(5, 50, by = 5)## [1] 5 10 15 20 25 30 35 40 45 50
seq(5, 50, length.out = 5)## [1] 5.00 16.25 27.50 38.75 50.00
paste(1:10, 20:30, sep = "-")## [1] "1-20" "2-21" "3-22" "4-23" "5-24" "6-25" "7-26" "8-27" "9-28" "10-29" "1-30"
paste(1:10, collapse = "-")## [1] "1-2-3-4-5-6-7-8-9-10"
paste0("x", 1:10)## [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9" "x10"
min(1:10)## [1] 1
max(1:10)## [1] 10
range(1:10)## [1] 1 10
mean(1:10)## [1] 5.5
sd(1:10)## [1] 3.02765
Custom Functions
Users can also create their own functions
customFunction1 <- function(x, y) {
z <- 100 * x / (x + y)
paste(z, "%")
}
customFunction1(x = 10, y = 90)## [1] "10 %"
customFunction2 <- function(x) {
mymin <- mean(x - sd(x))
mymax <- mean(x) + sd(x)
print(paste("Min =", mymin))
print(paste("Max =", mymax))
}
customFunction2(x = 1:10)## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"
for loops and if else
statements
xx <- NULL #creates and empty object
for(i in 1:10) {
xx[i] <- i*3
}
xx## [1] 3 6 9 12 15 18 21 24 27 30
xx %% 2 #gives the remainder when divided by 2## [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
if((xx[i] %% 2) == 0) {
print(paste(xx[i],"is Even"))
} else {
print(paste(xx[i],"is Odd"))
}
}## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
# or
ifelse(xx %% 2 == 0, "Even", "Odd")## [1] "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even"
paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))## [1] "3 is Odd" "6 is Even" "9 is Odd" "12 is Even" "15 is Odd" "18 is Even" "21 is Odd" "24 is Even" "27 is Odd" "30 is Even"
Objects
Information can be stored in user defined objects, in multiple forms:
c(): a string of valuesmatrix(): a two dimensional matrix in one formatdata.frame(): a two dimensional matrix where each column can be a different formatlist():
A string…
xc <- 1:10
xc## [1] 1 2 3 4 5 6 7 8 9 10
xc <- c(1,2,3,4,5,6,7,8,9,10)
xc## [1] 1 2 3 4 5 6 7 8 9 10
A matrix…
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
A data frame…
xd <- data.frame(
x1 = c("aa","bb","cc","dd","ee",
"ff","gg","hh","ii","jj"),
x2 = 1:10,
x3 = c(1,1,1,1,1,2,2,2,3,3),
x4 = rep(c(1,2), times = 5),
x5 = rep(1:5, times = 2),
x6 = rep(1:5, each = 2),
x7 = seq(5, 50, by = 5),
x8 = log10(1:10),
x9 = (1:10)^3,
x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
A list…
xl <- list(xc, xm, xd)
xl[[1]]## [1] 1 2 3 4 5 6 7 8 9 10
xl[[2]]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
xl[[3]]## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
Selecting Data
xc[5] # 5th element in xc## [1] 5
xd$x3[5] # 5th element in col "x3"## [1] 1
xd[5,"x3"] # row 5, col "x3"## [1] 1
xd$x3 # all of col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[,"x3"] # all rows, col "x3"## [1] 1 1 1 1 1 2 2 2 3 3
xd[3,] # row 3, all cols## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"## x4 x5
## 2 2 2
## 4 2 4
xl[[3]]$x1 # 3rd object in the list, col "x1## [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"
regexpr
xx <- data.frame(Name = c("Item 1 (detail 1)",
"Item 20 (detail 20)",
"Item 300 (detail 300)"),
Item = NA,
Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx## Name Item Detail
## 1 Item 1 (detail 1) Item 1 detail 1
## 2 Item 20 (detail 20) Item 20 detail 20
## 3 Item 300 (detail 300) Item 300 detail 300
Data Formats
Data can also be saved in many formats:
- numeric
- integer
- character
- factor
- logical
xd$x3 <- as.character(xd$x3)
xd$x3## [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
xd$x3 <- as.numeric(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
xd$x3 <- as.factor(xd$x3)
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
xd$x10## [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
as.numeric(xd$x10) # TRUE = 1, FALSE = 0## [1] 1 1 1 0 0 1 1 0 0 0
sum(xd$x10)## [1] 5
Internal structure of an object can be checked with
str()
str(xc) # c()## num [1:10] 1 2 3 4 5 6 7 8 9 10
str(xm) # matrix()## int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
str(xd) # data.frame()## 'data.frame': 10 obs. of 10 variables:
## $ x1 : chr "aa" "bb" "cc" "dd" ...
## $ x2 : int 1 2 3 4 5 6 7 8 9 10
## $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
## $ x4 : num 1 2 1 2 1 2 1 2 1 2
## $ x5 : int 1 2 3 4 5 1 2 3 4 5
## $ x6 : int 1 1 2 2 3 3 4 4 5 5
## $ x7 : num 5 10 15 20 25 30 35 40 45 50
## $ x8 : num 0 0.301 0.477 0.602 0.699 ...
## $ x9 : num 1 8 27 64 125 216 343 512 729 1000
## $ x10: logi TRUE TRUE TRUE FALSE FALSE TRUE ...
str(xl) # list()## List of 3
## $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## $ :'data.frame': 10 obs. of 10 variables:
## ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
## ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
## ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
## ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
## ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
## ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
## ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
## ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
## ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...
Packages
Additional libraries can be installed and loaded for use.
install.packages("scales")library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx## Values Rescaled
## 1 1 1.000000
## 2 2 4.222222
## 3 3 7.444444
## 4 4 10.666667
## 5 5 13.888889
## 6 6 17.111111
## 7 7 20.333333
## 8 8 23.555556
## 9 9 26.777778
## 10 10 30.000000
libraries can also be used without having to load them
scales::rescale(1:10, to = c(1,30))## [1] 1.000000 4.222222 7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000
Data Wrangling
R for Data Science - https://r4ds.had.co.nz/
xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
Data1 = 1:10,
Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 X 6 60 66 6000
## 7 X 7 70 77 7000
## 8 X 8 80 88 8000
## 9 Y 9 90 99 9000
## 10 Y 10 100 110 10000
xx$Data1 < 5 # which are less than 5## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
xx[xx$Data1 < 5,]## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx[xx$Group == "X", c("Group","Data2","NewData1")]## Group Data2 NewData1
## 1 X 10 11
## 2 X 20 22
## 6 X 60 66
## 7 X 70 77
## 8 X 80 88
Data wrangling with tidyverse and pipes
(%>%)
library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
mutate(Data1 = 1:10,
Data2 = seq(10, 100, by = 10),
NewData1 = Data1 + Data2,
NewData2 = Data1 * 1000)
xx## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 Y 6 60 66 6000
## 7 Y 7 70 77 7000
## 8 X 8 80 88 8000
## 9 X 9 90 99 9000
## 10 X 10 100 110 10000
filter(xx, Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Data1 < 5)## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
xx %>% filter(Group == "X") %>%
select(Group, NewColName=Data2, NewData1)## Group NewColName NewData1
## 1 X 10 11
## 2 X 20 22
## 3 X 80 88
## 4 X 90 99
## 5 X 100 110
xs <- xx %>%
group_by(Group) %>%
summarise(Data2_mean = mean(Data2),
Data2_sd = sd(Data2),
NewData2_mean = mean(NewData2),
NewData2_sd = sd(NewData2))
xs## # A tibble: 2 × 5
## Group Data2_mean Data2_sd NewData2_mean NewData2_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X 60 41.8 6000 4183.
## 2 Y 50 15.8 5000 1581.
xx %>% left_join(xs, by = "Group")## Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1 X 1 10 11 1000 60 41.83300 6000 4183.300
## 2 X 2 20 22 2000 60 41.83300 6000 4183.300
## 3 Y 3 30 33 3000 50 15.81139 5000 1581.139
## 4 Y 4 40 44 4000 50 15.81139 5000 1581.139
## 5 Y 5 50 55 5000 50 15.81139 5000 1581.139
## 6 Y 6 60 66 6000 50 15.81139 5000 1581.139
## 7 Y 7 70 77 7000 50 15.81139 5000 1581.139
## 8 X 8 80 88 8000 60 41.83300 6000 4183.300
## 9 X 9 90 99 9000 60 41.83300 6000 4183.300
## 10 X 10 100 110 10000 60 41.83300 6000 4183.300
Read/Write data
xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)For excel sheets, the package readxl can be used to read
in sheets of data.
library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")Tidy Data
Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html
yy <- xx %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = round(mean(DTF),1)) %>%
arrange(Location)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name Location Mean_DTF
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
## <chr> <dbl> <dbl> <dbl>
## 1 CDC Maxim AGL 86.7 134. 52.5
## 2 ILL 618 AGL 79.3 138. 47
## 3 Laird AGL 76.8 137. 56.8
yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy## # A tibble: 9 × 3
## # Groups: Name [3]
## Name TraitName Value
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
yy <- yy %>% spread(key = Name, value = Value)
yy## # A tibble: 3 × 4
## TraitName `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
## <chr> <dbl> <dbl> <dbl>
## 1 Jessore, Bangladesh 86.7 79.3 76.8
## 2 Metaponto, Italy 134. 138. 137.
## 3 Saskatoon, Canada 52.5 47 56.8
Base Plotting
We will start with some basic plotting using the base function
plot()
Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs
Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html
# A basic scatter plot
plot(x = xd$x8, y = xd$x9)# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)# Adjust plot type
plot(x = xd$x8, y = xd$x9, type = "line")# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")Now lets create some random and normally distributed data to make some more complicated plots
# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru## [1] 25.193385 98.504574 95.704376 41.806885 52.442177 50.665790 70.061002 52.567294 35.101164 30.184006 2.741207 58.429539 55.144672 88.179157
## [15] 57.619787 6.782648 29.600660 32.518578 81.098274 10.904882 88.560835 51.695202 94.313438 66.467893 78.787727 43.023239 84.984499 68.667746
## [29] 51.295470 17.900599 24.927597 10.868688 6.466176 43.064929 26.934207 50.056961 59.240597 7.184548 91.255165 78.609234 22.007569 68.196995
## [43] 82.755001 79.729819 71.695944 95.769866 7.935079 40.174607 17.708639 22.838842 16.877840 16.191370 38.186461 59.229540 88.718322 46.245999
## [57] 92.527099 14.641899 60.707335 97.233799 88.984460 71.461659 96.028224 70.751343 28.127407 31.264564 62.070090 90.604760 13.317171 99.604520
## [71] 6.655749 88.795790 97.983416 63.270739 87.054058 17.463765 43.019854 15.528810 59.444450 95.735790 95.874147 88.282018 93.294005 14.109499
## [85] 53.620020 55.635577 84.965375 41.217962 73.174817 75.531356 29.316709 45.675513 78.096887 64.858853 97.531620 36.472314 97.817469 48.593394
## [99] 75.838881 73.555024
plot(x = ru)order(ru)## [1] 11 33 71 16 38 47 32 20 69 84 58 78 52 51 76 49 30 41 50 31 1 35 65 91 17 10 66 18 9 96 53 48 88 4 77 26 34
## [38] 92 56 98 36 6 29 22 5 8 85 13 86 15 12 54 37 79 59 67 74 94 24 42 28 7 64 62 45 89 100 90 99 93 40 25 44 19
## [75] 43 87 27 75 14 82 21 55 72 61 68 39 57 83 23 3 80 46 81 63 60 95 97 73 2 70
ru<- ru[order(ru)]
ru## [1] 2.741207 6.466176 6.655749 6.782648 7.184548 7.935079 10.868688 10.904882 13.317171 14.109499 14.641899 15.528810 16.191370 16.877840
## [15] 17.463765 17.708639 17.900599 22.007569 22.838842 24.927597 25.193385 26.934207 28.127407 29.316709 29.600660 30.184006 31.264564 32.518578
## [29] 35.101164 36.472314 38.186461 40.174607 41.217962 41.806885 43.019854 43.023239 43.064929 45.675513 46.245999 48.593394 50.056961 50.665790
## [43] 51.295470 51.695202 52.442177 52.567294 53.620020 55.144672 55.635577 57.619787 58.429539 59.229540 59.240597 59.444450 60.707335 62.070090
## [57] 63.270739 64.858853 66.467893 68.196995 68.667746 70.061002 70.751343 71.461659 71.695944 73.174817 73.555024 75.531356 75.838881 78.096887
## [71] 78.609234 78.787727 79.729819 81.098274 82.755001 84.965375 84.984499 87.054058 88.179157 88.282018 88.560835 88.718322 88.795790 88.984460
## [85] 90.604760 91.255165 92.527099 93.294005 94.313438 95.704376 95.735790 95.769866 95.874147 96.028224 97.233799 97.531620 97.817469 97.983416
## [99] 98.504574 99.604520
plot(x = ru)# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd## [1] 42.15130 41.61166 52.30228 43.38781 57.66050 47.70939 53.69369 63.62335 30.90672 47.30393 42.69210 51.53065 41.88363 51.87624 66.97323 62.14182
## [17] 46.95019 40.62136 37.78827 60.36493 38.60043 53.15806 56.62551 29.74607 38.03285 42.36447 57.06455 32.78520 75.77950 60.20017 44.26458 70.80339
## [33] 46.57787 69.27993 42.95529 58.97487 35.82687 34.64084 60.24398 58.86121 51.44672 30.83397 49.66865 41.01297 45.58562 49.87876 69.26748 55.70696
## [49] 45.30250 49.46617 41.08284 48.66546 50.75803 27.92003 53.07214 58.86586 46.07446 39.91056 52.70106 46.35478 38.57026 63.90551 67.78631 55.90365
## [65] 39.77055 48.46846 50.32353 49.96777 52.43296 58.81848 47.66891 39.60114 54.86165 36.94703 62.48773 54.75267 32.01747 62.27615 61.06503 65.43478
## [81] 58.94182 54.75291 58.57888 54.03810 65.71231 54.90597 67.94244 48.48824 49.95483 51.83445 38.62095 48.40683 45.10291 51.56530 55.48362 66.48564
## [97] 43.03963 54.13608 74.35664 59.06931
nd <- nd[order(nd)]
nd## [1] 27.92003 29.74607 30.83397 30.90672 32.01747 32.78520 34.64084 35.82687 36.94703 37.78827 38.03285 38.57026 38.60043 38.62095 39.60114 39.77055
## [17] 39.91056 40.62136 41.01297 41.08284 41.61166 41.88363 42.15130 42.36447 42.69210 42.95529 43.03963 43.38781 44.26458 45.10291 45.30250 45.58562
## [33] 46.07446 46.35478 46.57787 46.95019 47.30393 47.66891 47.70939 48.40683 48.46846 48.48824 48.66546 49.46617 49.66865 49.87876 49.95483 49.96777
## [49] 50.32353 50.75803 51.44672 51.53065 51.56530 51.83445 51.87624 52.30228 52.43296 52.70106 53.07214 53.15806 53.69369 54.03810 54.13608 54.75267
## [65] 54.75291 54.86165 54.90597 55.48362 55.70696 55.90365 56.62551 57.06455 57.66050 58.57888 58.81848 58.86121 58.86586 58.94182 58.97487 59.06931
## [81] 60.20017 60.24398 60.36493 61.06503 62.14182 62.27615 62.48773 63.62335 63.90551 65.43478 65.71231 66.48564 66.97323 67.78631 67.94244 69.26748
## [97] 69.27993 70.80339 74.35664 75.77950
plot(x = nd)hist(x = nd)hist(nd, breaks = 20, col = "darkgreen")plot(x = density(nd))boxplot(x = nd)boxplot(x = nd, horizontal = T)ggplot2
Lets be honest, the base plots are ugly! The ggplot2
package gives the user to create a better, more visually appealing
plots. Additional packages such as ggbeeswarm and
ggrepel also contain useful functions to add to the
functionality of ggplot2.
ggplot2 - https://ggplot2.tidyverse.org/
Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html
library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()mp + geom_point(aes(color = x3, shape = x3), size = 4)mp + geom_line(size = 2)mp + geom_line(aes(color = x3), size = 2)mp + geom_smooth(method = "loess")mp + geom_smooth(method = "lm")xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
rnorm(50, mean = 60, sd = 5)),
group = factor(rep(1:2, each = 50)),
label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")mp + geom_histogram(color = "black", position = "dodge")mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1mp + geom_density(alpha = 0.5)mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")mp + geom_boxplot() + geom_point()mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")library(ggbeeswarm)
mp + geom_quasirandom()mp + geom_quasirandom(aes(shape = group))mp2 <- mp + geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
common.legend = T, legend = "bottom")Statistics
Handbook of Biological Statistics - http://biostathandbook.com/
R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html
# Prep data
lev_Loc <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
mutate(Location = factor(Location, levels = lev_Loc),
Name = factor(Name, levels = lev_Name))
xx <- dd %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
## <fct> <dbl> <dbl> <dbl>
## 1 ILL 618 AGL 47 79.3 138.
## 2 CDC Maxim AGL 52.5 86.7 134.
## 3 Laird AGL 56.8 76.8 137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF,
color = Name, group = Name, shape = Name)) +
geom_point(size = 2.5, alpha = 0.7) +
geom_line(size = 1, alpha = 0.7) +
theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.
summary(aov(DTF ~ Name * Location, data = dd))## Df Sum Sq Mean Sq F value Pr(>F)
## Name 2 88 44 3.476 0.0395 *
## Location 2 65863 32931 2598.336 < 2e-16 ***
## Name:Location 4 560 140 11.044 2.52e-06 ***
## Residuals 45 570 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.
If we only have two things to compare, we could do a t-test.
xx <- dd %>%
filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)##
## Welch Two Sample t-test
##
## data: xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32.18265 -25.48402
## sample estimates:
## mean of x mean of y
## 52.11111 80.94444
DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.
xx <- dd %>%
filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
Location == "Metaponto, Italy") %>%
spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)##
## Welch Two Sample t-test
##
## data: xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059739 7.059739
## sample estimates:
## mean of x mean of y
## 137.8333 136.8333
DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.
pch Plot
xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
y = rep(5:1, each = 6, length.out = 26),
pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
geom_point(color = "darkred", fill = "darkblue", size = 5) +
geom_text(aes(label = pch), nudge_x = -0.25) +
scale_shape_manual(values = xx$pch) +
scale_x_continuous(breaks = 6:1) +
scale_y_continuous(breaks = 6:1) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Plot symbols in R (pch)",
subtitle = "color = \"darkred\", fill = \"darkblue\"",
x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")R Markdown
Tutorials on how to create an R markdown document like this one can be found here:
- https://rmarkdown.rstudio.com/articles_intro.html
- https://rmarkdown.rstudio.com/lesson-1.html
- https://alexd106.github.io/intro2R/Rmarkdown_intro.html